class_docstr: Hamilton is a class that provides the LinearOperator of the Hamiltonian
  components.
  
  The Hamiltonian represents the total energy operator for a system of
  interacting electrons. The Kohn-Sham DFT approach introduces a set of
  fictitious non-interacting electrons that move in an effective potential.
  The total energy functional, which includes the kinetic energy of these
  fictitious electrons and their interaction with an effective potential
  (including the electron-electron interaction), is minimized to obtain the
  ground-state electronic structure.
  
  The Kohn-Sham Hamiltonian is a key component of this approach, representing
  the operator that governs the evolution of the Kohn-Sham orbitals. It
  includes terms for the kinetic energy of electrons, the external potential
  (usually from nuclei), and the exchange-correlation potential that accounts
  for the electron-electron interactions.
  
  The Fock matrix represents the one-electron part of the Hamiltonian matrix. Its
  components include kinetic energy, nuclear attraction, and electron-electron
  repulsion integrals. The Fock matrix is pivotal in solving the electronic
  Schrödinger equation and determining the electronic structure of molecular
  systems.
  
  Examples
  --------
  >>> from deepchem.utils.dft_utils import BaseHamilton
  >>> class MyHamilton(BaseHamilton):
  ...    def __init__(self):
  ...        self._nao = 2
  ...        self._kpts = torch.tensor([[0.0, 0.0, 0.0]])
  ...        self._df = None
  ...    @property
  ...    def nao(self):
  ...        return self._nao
  ...    @property
  ...    def kpts(self):
  ...        return self._kpts
  ...    @property
  ...    def df(self):
  ...        return self._df
  ...    def build(self):
  ...        return self
  ...    def get_nuclattr(self):
  ...        return torch.ones((1, 1, self.nao, self.nao))
  >>> ham = MyHamilton()
  >>> hamilton = ham.build()
  >>> hamilton.get_nuclattr()
  tensor([[[[1., 1.],
            [1., 1.]]]])
docstr: Return the paramnames
  
  Parameters
  ----------
  methodname: str
      The name of the method.
  prefix: str (default "")
      The prefix of the paramnames.
  
  Returns
  -------
  List[str]
      The paramnames.
docstr_tok: ["Return","the","paramnames","Parameters","--","--","--","--","--","methodname",":","str","The","name","of","the","method",".","prefix",":","str","(","default","``","''",")","The","prefix","of","the","paramnames",".","Returns","--","--","--","-","List","[","str","]","The","paramnames","."]
code: def getparamnames(
      self, methodname: str, prefix: str = ""
  ) -> List[str]:
      """Return the paramnames
  
      Parameters
      ----------
      methodname: str
          The name of the method.
      prefix: str (default "")
          The prefix of the paramnames.
  
      Returns
      -------
      List[str]
          The paramnames.
  
      """
      pass
  
imports: from __future__ import annotations
  import torch
  from abc import abstractmethod, abstractproperty
  from typing import List, Optional, Union, Tuple
  from deepchem.utils.dft_utils import BaseGrid, BaseXC, BaseDF, SpinParam
  from deepchem.utils.differentiation_utils import EditableModule, LinearOperator
label_desc: Use image node_id 21 for calling the BaseHamilton obj's underlying member method code with example usage: obj.getparamnames(methodname, prefix) without return types